K-Nearest Neighbor

What Does K-Nearest Neighbor Mean?

A k-nearest-neighbor algorithm, often abbreviated k-nn, is an approach to data classification that estimates how likely a data point is to be a member of one group or the other depending on what group the data points nearest to it are in.

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The k-nearest-neighbor is an example of a “lazy learner” algorithm, meaning that it does not build a model using the training set until a query of the data set is performed.

Techopedia Explains K-Nearest Neighbor

A k-nearest-neighbor is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.

An algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.

The k-nearest-neighbor is an example of a “lazy learner” algorithm because it does not generate a model of the data set beforehand. The only calculations it makes are when it is asked to poll the data point’s neighbors. This makes k-nn very easy to implement for data mining.

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Margaret Rouse

Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…